Measuring directed functional connectivity using non-parametric directionality analysis: Validation and comparison with non-parametric Granger Causality

• Published in: NeuroImage (Orlando, Fla.) Vol. 218; p. 116796
• Main Authors: West, Timothy O., Halliday, David M., Bressler, Steven L., Farmer, Simon F., Litvak, Vladimir
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-09-2020; Elsevier Limited; Academic Press; Elsevier
• Subjects: Algorithms; Brain - physiology; Causality; Computational neuroscience; Computer Simulation; Directionality; EEG; Estimates; Functional connectivity; Humans; Local field potentials; Magnetoencephalography; MEG; Methods; Models, Neurological; Multimodal data; Nerve Net - physiology; Neural networks; Neuroimaging; Noise; Nonparametric statistics; Signal-to-noise; Volume conduction; Functional connectivity; Directionality; Neural networks; Computational neuroscience; Local field potentials; EEG; Signal-to-noise; Multimodal data; Volume conduction; MEG
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.116796

‘Non-parametric directionality’ (NPD) is a novel method for estimation of directed functional connectivity (dFC) in neural data. The method has previously been verified in its ability to recover causal interactions in simulated spiking networks in Halliday et al. (2015).
This work presents a validation of NPD in continuous neural recordings (e.g. local field potentials). Specifically, we use autoregressive models to simulate time delayed correlations between neural signals. We then test for the accurate recovery of networks in the face of several confounds typically encountered in empirical data. We examine the effects of NPD under varying: a) signal-to-noise ratios, b) asymmetries in signal strength, c) instantaneous mixing, d) common drive, e) data length, and f) parallel/convergent signal routing. We also apply NPD to data from a patient who underwent simultaneous magnetoencephalography and deep brain recording.
We demonstrate that NPD can accurately recover directed functional connectivity from simulations with known patterns of connectivity. The performance of the NPD measure is compared with non-parametric estimators of Granger causality (NPG), a well-established methodology for model-free estimation of dFC. A series of simulations investigating synthetically imposed confounds demonstrate that NPD provides estimates of connectivity that are equivalent to NPG, albeit with an increased sensitivity to data length. However, we provide evidence that: i) NPD is less sensitive than NPG to degradation by noise; ii) NPD is more robust to the generation of false positive identification of connectivity resulting from SNR asymmetries; iii) NPD is more robust to corruption via moderate amounts of instantaneous signal mixing.
The results in this paper highlight that to be practically applied to neural data, connectivity metrics should not only be accurate in their recovery of causal networks but also resistant to the confounding effects often encountered in experimental recordings of multimodal data. Taken together, these findings position NPD at the state-of-the-art with respect to the estimation of directed functional connectivity in neuroimaging.
•Non-parametric directionality (NPD) is a novel directed connectivity measure.•NPD estimates are equivalent to estimates of Granger causality but are more robust to signal confounds.•Multivariate extensions of NPD can correctly identify signal routing.